ClinicTrace AI: Intelligent Clinical Support System for the Integration, Traceability, and Evolutionary Analysis of Medical Information
DOI:
https://doi.org/10.70577/asce.v5i3.999Keywords:
artificial intelligence; clinical decision support systems; clinical traceability; electronic health records; continuity of care; contextual information retrievalAbstract
The increasing digitalization of healthcare services has significantly expanded the volume of clinical information that healthcare professionals must interpret during patient care. Clinical records, laboratory results, pharmacological treatments, and longitudinal medical histories are frequently distributed across different systems, making contextual analysis and continuity of care more difficult. In response to this challenge, the objective of this study was to develop and functionally validate ClinicTrace AI, an intelligent clinical support system designed to integrate, organize, and contextualize medical information through artificial intelligence.
This research adopted a mixed methodological approach with a descriptive-technological scope. The development process comprised requirements analysis, architectural design, and functional validation using real clinical cases to assess contextual information retrieval, longitudinal traceability, and AI-assisted generation of clinical recommendations.
The results demonstrated that the proposed platform successfully centralized clinical information from multiple sources, generated intelligent patient summaries, retrieved relevant medical history according to context, and facilitated longitudinal follow-up during healthcare processes. In addition, the system strengthened the organization of clinical information while supporting medical interpretation without replacing professional judgment.
It is concluded that the proposed technological tool represents a complementary clinical support approach that enhances continuity of care through intelligent clinical memory, longitudinal traceability, and contextual retrieval of medical information, thereby providing a technological contribution to decision support within digital healthcare environments
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References
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2022). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2022, baac026. https://doi.org/10.1093/database/baac026
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., et al. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689. https://doi.org/10.1186/s12909-023-04698-z
Bohr, A., & Memarzadeh, K. (2022). The rise of artificial intelligence in healthcare applications. En Artificial Intelligence in Healthcare (pp. 25–60). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Cajas-Palma, P. M., & Ortiz-Andrade, E. J. (2025). Actitud del personal médico ante la inteligencia artificial y su aplicación en la práctica clínica. Revista Arbitrada Interdisciplinaria Koinonía. https://doi.org/10.35381/r.k.v10i1.4790
Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023). Evaluating the feasibility of ChatGPT in healthcare: An analysis of multiple clinical and research scenarios. Journal of Medical Systems, 47(1), 33. https://doi.org/10.1007/s10916-023-01925-4
Gomez-Cabello, C. A., Muñoz-Molina, Y., Moya-Gómez, B., et al. (2024). A scoping review of current clinical implementations of AI-based clinical decision support systems in primary healthcare. Healthcare, 12(7), 714. https://doi.org/10.3390/healthcare12070714
Hassan, A., Khan, M., & colaboradores. (2024). Artificial Intelligence in Healthcare: Applications, Challenges and Future Directions. Journal of Computer Science and Technology Studies, 6(3). https://doi.org/10.32996/jcsts.2024.6.3.12
Hassan, N., et al. (2024). Systematic review to understand users’ perspectives on AI-enabled decision aids to inform shared decision making. npj Digital Medicine. https://doi.org/10.1038/s41746-024-01326-y
Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024). Artificial intelligence and decision-making in healthcare: A thematic analysis of a systematic review of reviews. Health Services Insights, 17, 1–13. https://doi.org/10.1177/23333928241234863
Lalama-Flores, M. A., Lalama-Gavilánez, M. S., López-Barrionuevo, C. G., & Reyes-Pérez, M. A. (2025). Perspectiva de los profesionales de la salud ante adopción de inteligencia artificial en la medicina. Revista Metropolitana de Ciencias Aplicadas, 8(2), 66–73. https://doi.org/10.62452/rn2d5e60
Moazemi, S., et al. (2023). Artificial intelligence for clinical decision support and selected applications in cardiovascular medicine. Frontiers in Medicine. https://doi.org/10.3389/fmed.2023.1109411
Muneeb, M., Ahmad, M., Alharbi, A., Alzahrani, M., Alshammari, F., & Alotaibi, Y. (2025). Artificial Intelligence in Clinical Decision Support Systems: Current Applications, Challenges, and Future Directions. Healthcare, 13(17), 2154. https://doi.org/10.3390/healthcare13172154
Nascimento, I. J. B., Marcolino, M. S., Abdulazeem, H. M., et al. (2023). Impact of artificial intelligence on clinical decision-making: A systematic review. BMJ Health & Care Informatics, 30(1), e100748. https://doi.org/10.1136/bmjhci-2023-100748
Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21, 125. https://doi.org/10.1186/s12911-021-01488-9
Topol, E. J. (2021). High-performance medicine: The convergence of human and artificial intelligence. Communications of the ACM, 64(3), 44–56. https://doi.org/10.1145/3423923
Wang, L., Liu, X., & Chen, Y. (2024). Artificial intelligence-driven clinical decision support systems in healthcare: Current applications and future directions. npj Digital Medicine, 7(1), 45. https://doi.org/10.1038/s41746-024-01092-x
Yang, Q., Steinfeld, A., Zimmerman, J., & Rosé, C. (2023). Investigating trust in AI-based healthcare systems and clinical support tools. International Journal of Human-Computer Studies, 176, 103056. https://doi.org/10.1016/j.ijhcs.2023.103056
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